仿人搜索策略在装配中的应用
Imitating Human Search Strategies for Assembly
摘要：
我们提出了一个示教学习方法教机器人执行搜索策略模仿人类的情况下，由于位置的不确定性对齐任务失败。该方法利用人类示范学习状态不变动力学模型和探索分布，捕获的搜索区域所覆盖的演示。我们提出了两种可供选择的算法计算搜索轨迹的探索分布，一个基于采样和另一个基于确定性遍历控制。我们通过动力学模型学习的力来增强搜索轨迹，以实现在力和位置域中的搜索。叠加力的阻抗控制器用于再现学习的策略。我们在KUKA LWR 4+上进行了实验评估，以执行2D钉孔和3D电力插座任务。结果表明，所提出的方法只需很少的人类演示，就可以学会完成搜索任务。
英文摘要：
We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncertainty. The method utilizes human demonstrations to learn both a state invariant dynamics model and an exploration distribution that captures the search area covered by the demonstrator. We present two alternative algorithms for computing a search trajectory from the exploration distribution, one based on sampling and another based on deterministic ergodic control. We augment the search trajectory with forces learnt through the dynamics model to enable searching both in force and position domains. An impedance controller with superposed forces is used for reproducing the learnt strategy. We experimentally evaluate the method on a KUKA LWR4+ performing a 2D peg-in-hole and a 3D electricity socket task. Results show that the proposed method can, with only few human demonstrations, learn to complete the search task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
